Abstract
Recently, the slime mould algorithm (SMA) has become popular in function optimization, because it effectively uses exploration and exploitation to reach an optimal solution or near-optimal solution. However, the SMA uses two random search agents from the whole population to decide the future displacement and direction from the best search agents, which limits its exploitation and exploration. To solve this problem, we investigate an adaptive approach to decide whether opposition-based learning (OBL) will be used or not. Sometimes, the OBL is used to further increase the exploration. In addition, it maximizes the exploitation by replacing one random search agent with the best one in the position updating. The suggested technique is called an adaptive opposition slime mould algorithm (AOSMA). The qualitative and quantitative analysis of AOSMA is reported using 29 test functions that consisting of 23 classical test functions and 6 recently used composition functions from the IEEE CEC 2014 test suite. The results are compared with state-of-the-art optimization methods. Results presented in this paper show that AOSMA’s performance is better than other optimization algorithms. The AOSMA is evaluated using Wilcoxon’s rank-sum test. It also ranked one in Friedman’s mean rank test. The proposed AOSMA algorithm would be useful for function optimization to solve real-world engineering problems.
This is a preview of subscription content, log in via an institution to check access.
Similar content being viewed by others
References
Ahmadianfar I, Heidari AA, Gandomi AH et al (2021) RUN beyond the metaphor: an efficient optimization algorithm based on Runge Kutta method. Expert Syst Appl 181:115079. https://doi.org/10.1016/j.eswa.2021.115079
Dhargupta S, Ghosh M, Mirjalili S, Sarkar R (2020) Selective opposition based grey wolf optimization. Expert Syst Appl 151:113389. https://doi.org/10.1016/j.eswa.2020.113389
Dorigo M, Stützle T (2004) Ant colony optimization. MIT Press, Cambridge
Faramarzi A, Afshar M (2014) A novel hybrid cellular automata-linear programming approach for the optimal sizing of planar truss structures. Civ Eng Environ Syst 31:209–228. https://doi.org/10.1080/10286608.2013.820280
Faramarzi A, Heidarinejad M, Stephens B, Mirjalili S (2020) Equilibrium optimizer: a novel optimization algorithm. Knowl-Based Syst 191:105190. https://doi.org/10.1016/j.knosys.2019.105190
Feoktistov V (2006) Differential evolution. Springer, New York
Glover F (1989) Tabu search—part I. ORSA J Comput 1:190–206. https://doi.org/10.1287/ijoc.1.3.190
Glover F (1990) Tabu search—part II. ORSA J Comput 2:4–32. https://doi.org/10.1287/ijoc.2.1.4
Guha R, Ghosh M, Mutsuddi S et al (2020) Embedded chaotic whale survival algorithm for filter–wrapper feature selection. Soft Comput. https://doi.org/10.1007/s00500-020-05183-1
Heidari AA, Mirjalili S, Faris H et al (2019) Harris hawks optimization: algorithm and applications. Future Gener Comput Syst 97:849–872. https://doi.org/10.1016/j.future.2019.02.028
Holland JH (1975) Adaptation in natural and artificial systems. University of Michigan Press, Michigan
Jain M, Singh V, Rani A (2019) A novel nature-inspired algorithm for optimization: Squirrel search algorithm. Swarm Evol Comput 44:148–175. https://doi.org/10.1016/j.swevo.2018.02.013
Karaboga D, Basturk B (2008) On the performance of artificial bee colony (ABC) algorithm. Appl Soft Comput 8:687–697. https://doi.org/10.1016/j.asoc.2007.05.007
Kennedy J, Eberhart R (1995) Particle swarm optimization. In: Neural networks, 1995. Proceedings., IEEE International Conference on, vol 4. pp 1942–1948
Kirkpatrick S, Gelatt CD, Vecchi MP (1983) Optimization by simulated annealing. Science (80- ) 220:671. https://doi.org/10.1126/science.220.4598.671
Li S, Chen H, Wang M et al (2020) Slime mould algorithm: a new method for stochastic optimization. Future Gener Comput Syst. https://doi.org/10.1016/j.future.2020.03.055
Liang JJ, Qu BY, Suganthan PN (2013) Problem definitions and evaluation criteria for the CEC 2014 special session and competition on single objective real-parameter numerical optimization
Liu Y, Heidari AA, Ye X et al (2021) Boosting slime mould algorithm for parameter identification of photovoltaic models. Energy 234:121164. https://doi.org/10.1016/j.energy.2021.121164
Mahdavi S, Rahnamayan S, Deb K (2018) Opposition based learning: a literature review. Swarm Evol Comput 39:1–23. https://doi.org/10.1016/j.swevo.2017.09.010
Mirjalili S, Lewis A (2016) The Whale Optimization Algorithm. Adv Eng Softw 95:51–67. https://doi.org/10.1016/j.advengsoft.2016.01.008
Mirjalili S, Mirjalili SM, Lewis A (2014) Grey wolf optimizer. Adv Eng Softw 69:46–61. https://doi.org/10.1016/j.advengsoft.2013.12.007
Naik MK, Panda R (2016) A novel adaptive cuckoo search algorithm for intrinsic discriminant analysis based face recognition. Appl Soft Comput 38:661–675. https://doi.org/10.1016/j.asoc.2015.10.039
Naik MK, Wunnava A, Jena B, Panda R (2020) 1. Nature-inspired optimization algorithm and benchmark functions: a literature survey. In: Bisht DCS, Ram M (eds) Computational intelligence, 3rd edn. De Gruyter, Berlin, Boston, pp 1–26
Qian S, Wu H, Xu G (2020) An improved particle swarm optimization with clone selection principle for dynamic economic emission dispatch. Soft Comput 24:15249–15271. https://doi.org/10.1007/s00500-020-04861-4
Rao RV, Savsani VJ, Vakharia DP (2012) Teaching–learning-based optimization: an optimization method for continuous non-linear large scale problems. Inf Sci (ny) 183:1–15. https://doi.org/10.1016/j.ins.2011.08.006
Rashedi E, Nezamabadi-pour H, Saryazdi S (2009) GSA: a gravitational search algorithm. Inf Sci (ny) 179:2232–2248. https://doi.org/10.1016/j.ins.2009.03.004
Shadravan S, Naji HR, Bardsiri VK (2019) The sailfish optimizer: a novel nature-inspired metaheuristic algorithm for solving constrained engineering optimization problems. Eng Appl Artif Intell 80:20–34. https://doi.org/10.1016/j.engappai.2019.01.001
Talbi E-G (2009) Metaheuristics. John Wiley & Sons, Inc., Hoboken, NJ, USA
Tizhoosh HR (2005) Opposition-based learning: a new scheme for machine intelligence. In: International conference on computational intelligence for modelling, control and automation and international conference on intelligent agents, web technologies and internet commerce (CIMCA-IAWTIC’06). pp 695–701
Tu J, Chen H, Wang M, Gandomi AH (2021) The colony predation algorithm. J Bionic Eng 18:674–710. https://doi.org/10.1007/s42235-021-0050-y
Wang G-G (2018) Moth search algorithm: a bio-inspired metaheuristic algorithm for global optimization problems. Memetic Comput 10:151–164. https://doi.org/10.1007/s12293-016-0212-3
Wang G-G, Deb S, Cui Z (2019) Monarch Butterfly Optimization. Neural Comput Appl 31:1995–2014. https://doi.org/10.1007/s00521-015-1923-y
Wolpert DH, Macready WG (1997) No free lunch theorems for optimization. IEEE Trans Evol Comput 1:67–82. https://doi.org/10.1109/4235.585893
Wunnava A, Kumar Naik M, Panda R et al (2020a) A differential evolutionary adaptive Harris hawks optimization for two dimensional practical Masi entropy-based multilevel image thresholding. J King Saud Univ - Comput Inf Sci. https://doi.org/10.1016/j.jksuci.2020.05.001
Wunnava A, Naik MK, Panda R et al (2020b) An adaptive Harris hawks optimization technique for two dimensional grey gradient based multilevel image thresholding. Appl Soft Comput 95:106526. https://doi.org/10.1016/j.asoc.2020.106526
Wunnava A, Naik MK, Panda R et al (2020c) A novel interdependence based multilevel thresholding technique using adaptive equilibrium optimizer. Eng Appl Artif Intell 94:103836. https://doi.org/10.1016/j.engappai.2020.103836
Yang Y, Chen H, Heidari AA, Gandomi AH (2021) Hunger games search: Visions, conception, implementation, deep analysis, perspectives, and towards performance shifts. Expert Syst Appl 177:114864. https://doi.org/10.1016/j.eswa.2021.114864
Yang X-S (2014) Cuckoo search and firefly algorithm: overview and analysis. In: Yang X-S (ed) Cuckoo search and firefly algorithm. Springer, New York, pp 1–26
Yao X, Yong L, Guangming L (1999) Evolutionary programming made faster. Evol Comput IEEE Trans 3:82–102. https://doi.org/10.1109/4235.771163
Yu C, Heidari AA, Xue X et al (2021) Boosting quantum rotation gate embedded slime mould algorithm. Expert Syst Appl 181:115082. https://doi.org/10.1016/j.eswa.2021.115082
Zhao W, Zhang Z, Wang L (2020) Manta ray foraging optimization: An effective bio-inspired optimizer for engineering applications. Eng Appl Artif Intell 87:103300. https://doi.org/10.1016/j.engappai.2019.103300
Zhao S, Wang P, Heidari AA et al (2021) Multilevel threshold image segmentation with diffusion association slime mould algorithm and Renyi’s entropy for chronic obstructive pulmonary disease. Comput Biol Med 134:104427
Zhu F, Chen D, Zou F (2020) A novel hybrid dynamic fireworks algorithm with particle swarm optimization. Soft Comput. https://doi.org/10.1007/s00500-020-05308-6
Author information
Authors and Affiliations
Corresponding author
Ethics declarations
Conflict of interest
The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.
Additional information
Publisher's Note
Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.
Rights and permissions
About this article
Cite this article
Naik, M.K., Panda, R. & Abraham, A. Adaptive opposition slime mould algorithm. Soft Comput 25, 14297–14313 (2021). https://doi.org/10.1007/s00500-021-06140-2
Accepted:
Published:
Issue Date:
DOI: https://doi.org/10.1007/s00500-021-06140-2